Accelerating error correction in tomographic reconstruction

doi:10.1038/s43246-022-00267-x

Accelerating error correction in tomographic reconstruction

2022-07-11

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DOI：10.1038/s43246-022-00267-x

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Spurred by recent advances in detector technology and X-ray optics, upgrades to scanning-probe-based tomographic imaging have led to an exponential growth in the amount and complexity of experimental data and have created a clear opportunity for tomographic imaging to approach single-atom sensitivity. The improved spatial resolution, however, is highly susceptible to systematic and random experimental errors, such as center of rotation drifts, which may lead to imaging artifacts and prevent reliable data extraction. Here, we present a model-based approach that simultaneously optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. Our algorithm utilizes an adaptive regularizer that is dynamically adjusted at each alternating iteration step. Furthermore, we describe its implementation in a software package targeting high-throughput workflows for execution on distributed-memory clusters. We demonstrate the performance of our solver on large-scale synthetic problems and show that it is robust to a wide range of noise and experimental drifts with near-ideal throughput. Recent advances in scanning probe-based tomographic imaging have greatly improved spatial resolution, but systematic and random errors are a serious impediment to reliable data extraction. Here, a combined optimization and alignment algorithm provides a scalable approach to error-correcting reconstruction of large datasets.

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MICROSCOPE TILT SERIES

ELECTRON TOMOGRAPHY

AUTOMATIC ALIGNMENT

COMPUTED-TOMOGRAPHY

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Accelerating error correction in tomographic reconstruction

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